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Maximizing genetic gain for the sire line of a crossbreeding scheme utilizing both purebred and crossbred information

Published online by Cambridge University Press:  02 September 2010

P. Bijma  and
J. A. M. van Arendonk
P. Bijma
Affiliation:
Department of Animal Breeding, Wageningen Institute of Animal Sciences (WIAS), Wageningen Agricultural University, Wageningen, The Netherlands
J. A. M. van Arendonk
Affiliation:
Department of Animal Breeding, Wageningen Institute of Animal Sciences (WIAS), Wageningen Agricultural University, Wageningen, The Netherlands
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Abstract

A selection index procedure which utilizes both, purebred and crossbred information was developed for the sire line of a three-path crossbreeding scheme in pigs, to predict response to best linear unbiased prediction (BLUP) selection with an animal model. Purebred and crossbred performance were treated as correlated traits. The breeding goal was crossbred performance but methods can be applied to other goals. A hierarchical mating structure was used. Sires were mated to purebred dams to generate replacements and to F^ from the dam line to generate fattening pigs. Generations were discrete, inbreeding was ignored. The selection index included purebred and crossbred phenotypic information of the current generation and estimated breeding values for purebred and crossbred performance of parents and mates of parents from the previous generation. Reduction of genetic variance due to linkage disequilibrium and reduction of selection intensity due to finite population size and due to correlated index values was accounted for. Selection was undertaken until asymptotic responses were reached. The index was used to optimize the number of selected parents per generation and the number of offspring tested per litter, and to make inferences on the value of crossbred information when the breeding goal was crossbred performance. It was optimal to test a maximum number of offspring per litter, mainly due to increased female selection intensities. Maximum response reductions due to linkage disequilibrium and correlated index values were 32% and 29% respectively. Correcting for correlated index values changed ranking of breeding schemes. Benefit of crossbred information was largest when the genetic correlation between purebred and crossbred performance was low. Due to high correlations between index values in that case, the optimum number of selected sires increased considerably when crossbred information was included.

Keywords

best linear unbiased predictioncrossbreedinglinkage disequilibriumselection indexselection responses
Type
Research Article
Information
Animal Science , Volume 66 , Issue 2 , April 1998 , pp. 529 - 542
Copyright
Copyright © British Society of Animal Science 1998

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References

Baumung, R., Solkner, J. and Essl, A. 1997. Correlation between purebred and crossbred performance under a two-locus model with additive by additive interaction Journal of Animal Breeding and Genetics 114: 89–98.CrossRefGoogle Scholar
Belonsky, G. M. and Kennedy, B. W. 1988. Selection on individual phenotype and best linear unbiased predictor of breeding value in a closed swine herd. Journal of Animal Science 66: 1124–1131.Google Scholar
Boer, I. J. M. de and Arendonk, J. A. M. van. 1991. Genetic and clonal responses in closed dairy cattle nucleus schemes. Animal Production 53:1–9.Google Scholar
Brown, W. P. and Bell, A. E. 1980. An experimental comparison of selection alternatives to plateaued response Genetics 94: 477–496.CrossRefGoogle ScholarPubMed
Buhner, M. G. 1971. The effect of selection on genetic variability The American Naturalist 105: 201–211.Google Scholar
Burrows, P. M. 1972. Expected selection differentials for directional selection. Biometrics 28: 1091–1100.CrossRefGoogle ScholarPubMed
Dekkers, J. C. M. 1992. Asymptotic response to selection on best linear unbiased predictors of breeding values Animal Production 54: 351–360.Google Scholar
De Roo, G. 1987. A stochastic model to study breeding schemes in a small pig population Agricultural Systems 25: 1–25.Google Scholar
De Roo, G. 1988. Studies on breeding schemes in a closed pig population. 1. Population size and selection intensities. Livestock Production Science 19:417–441.CrossRefGoogle Scholar
Kennedy, B. W., Schaeffer, L. R. and Sorensen, D. A. 1988. Genetic properties of animal models. Journal of Dairy Science 71: (suppl. 2) 17–26.CrossRefGoogle Scholar
Kirsch, W., Fewson, D. and Fender, M. 1962. Priifungsanstalten als Hilfsmittel fiir die Selektion in der Schweinezucht. 1. Modellrechnungen iiber die Effektivitat verschiedener Priifungsmethoden und Priifungssysteme. Zeitschriftfur Tierzucht und Zuchtungsbiologie 77: 388–407.Google Scholar
Merks, J. W. M. 1986. Genotype × environment interactions in pig breeding programmes. 1. Central test. Livestock Production Science 14: 365–381.Google Scholar
Meuwissen, T. H. E. 1991. Reduction of selection differentials in finite populations with a nested full-half-sib family structure Biometrics 47:195–203.CrossRefGoogle Scholar
Meuwissen, T. H. E. 1997. Maximizing the response of selection with a predefined rate of inbreeding Journal of Animal Science 75: 934–940.Google Scholar
Phocas, F. and Colleau, J. J. 1995. Approximating selection differentials and variances for correlated selection indices. Genetics, Selection, Evolution 27: 551–565.Google Scholar
Rawlings, J. O. 1976. Order statistics for a special class of unequally correlated multinormal variates Biometrics 32: 875–887.Google Scholar
Villanueva, B., Wray, N. R. and Thompson, R. 1993. Prediction of asymptotic rates of response from selection on multiple traits using univariate and multivariate best linear unbiased predictors Animal Production 57:1–13.Google Scholar
Wei, M. and Steen, H. A. M. van der. 1991. Comparison of reciprocal recurrent selection with pure-line selection systems in animal breeding (a review) Animal Breeding Abstracts 59: 281–298.Google Scholar
Wei, M., Steen, H. A. M. van der, Werf, J. H. J. van der and Brascamp, E. W. 1991a. Relationship between purebred and crossbred parameters. 1. Variances and covariances under the one-locus model. Journal of Animal Breeding and Genetics 108: 253–261.Google Scholar
Wei, M. and Werf, J. H. J. van der. 1994. Maximizing genetic response in crossbreds using both purebred and crossbred information Animal Production 59: 401–413.Google Scholar
Wei, M., Werf, J. H. J. van der and Brascamp, E. W. 1991b. Relationship between purebred and crossbred parameters. 2. Genetic correlation between purebred and crossbred performance under the model with two loci. Journal of Animal Breeding and Genetics 108:262–269.CrossRefGoogle Scholar
Werf, J. H. J. van der. 1990. Models to estimate genetic parameters in crossbred dairy cattle populations under selection. Doctoral thesis, Wageningen Agricultural University.Google Scholar
Wray, N. R. and Hill, W. G. 1989. Asymptotic rates of response from index selection Animal Production 49: 217–227.Google Scholar
Wray, N. R. and Goddard, M. E. 1994. Increasing long-term response to selection. Genetics, Selection, Evolution 26: 431–;451.CrossRefGoogle Scholar